Programmable resistors can be utilized in a number of analog signal processing applications such as resistive ladders in analog to digital converters and in resistor arrays in neural networks. Neural networks provide a means for solving random problems, such as in real-world sensing systems which must "learn" from the surrounding environment so that solutions can improve with experience. Neural networks solve these sensing problems by expressing the sensor outputs as multi-dimensional vectors and then "learn" the vectors by constructing a matrix by correlation methods.
Neural networks use arrays formed by rows and columns of weighing elements, represented by resistors, to create the matrix vectors of voltages input from corresponding sensors using Ohm's law. Operational amplifiers sum the currents resulting from the drop of an input voltage across the resistors in each of the rows. The current output from each row represents the vector product for one component of a corresponding output vector. By storing the data in terms of resistor conductance values, an environment can be "learned" and later retrieved by associative recall. Thus, the resistors must first change with the system experience to "learn" an environment, but then remain fixed to recall the stored environment. Further, the neural network system can optimize the learned patterns of various environments by varying the resistive elements in the network.
Various means have been devised for providing variable weighing elements in neural networks. Each of these means has been found to have significant disadvantages. For example, circuitry using up-down counters and decoded switches along with fixed resistors may be used to generate the appropriate resistive weights, however, such an approach would be limited to only a small number of weighing elements, thereby limiting the complexity and utility of corresponding application. Electrically-erasable, electrically-programmable read-only memories (EEPROM) employing metal oxide semiconductor field effect transistors (MOSFETs) based on well known silicon structures provide another option. Silicon based devices, however, have significant limitations as to switching speed erasability lifetime, radiation tolerance and high temperature operation. Finally, dynamic random-access memories (DRAMs) have been considered, but DRAMs need refreshing after a read operation which greatly increases the number of overhead operations required in the overall scheme of the application.
Thus, the need has arisen for an improved programmable resistor having the capability to operate in demanding applications, such as neural networks.